The vehicle classification system developed by Federal Highway Administration (FHWA) of United States divides vehicle type into 13 categories depending on the number of axles and the wheelbase. However, establishing a fixed threshold for classifying a vehicle is difficult. The overlapping between vehicles pattern in the system needs a pattern recognition technique to distinguish between different vehicle categories. In this study, machine learning algorithms were used to classify various vehicles based on the collected traffic data from the embedded three-dimension Glass Fiber-Reinforced Polymer packaged Fiber Bragg Grating sensors (3D GFRP-FBG). The investigated machine learning algorithms include the support vector machines (SVM), Neural Network, and k-nearest neighbors (KNN) algorithms.


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    Title :

    Road vehicle classification using machine learning techniques


    Contributors:

    Conference:

    Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019 ; 2019 ; Denver,Colorado,United States


    Published in:

    Proc. SPIE ; 10970


    Publication date :

    2019-03-27





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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